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A human factors-aware assistance system in manufacturing based on gamification and hardware modularisation.

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Title: A human factors-aware assistance system in manufacturing based on gamification and hardware modularisation.
Authors: Ulmer, Jessica1 (AUTHOR) ulmer@fh-aachen.de, Braun, Sebastian1 (AUTHOR), Cheng, Chi-Tsun2 (AUTHOR), Dowey, Steve2 (AUTHOR), Wollert, Jörg1 (AUTHOR)
Source: International Journal of Production Research. Nov2023, Vol. 61 Issue 22, p7760-7775. 16p. 2 Color Photographs, 7 Diagrams, 1 Chart, 1 Graph.
Subjects: Manufacturing processes, Gamification, Architectural design, User experience, Human beings
Abstract: Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers' cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: A human factors-aware assistance system in manufacturing based on gamification and hardware modularisation.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Nov2023, Vol. 61 Issue 22, p7760-7775. 16p. 2 Color Photographs, 7 Diagrams, 1 Chart, 1 Graph.
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  Data: <searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Gamification%22">Gamification</searchLink><br /><searchLink fieldCode="DE" term="%22Architectural+design%22">Architectural design</searchLink><br /><searchLink fieldCode="DE" term="%22User+experience%22">User experience</searchLink><br /><searchLink fieldCode="DE" term="%22Human+beings%22">Human beings</searchLink>
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  Data: Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers' cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207543.2023.2166140
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        Text: English
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              Text: Nov2023
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